According to [1]}, there is an important distinction between the “intervention-based causality” and “statistical causality”. The first concept, introduced by Pearl [2]}, combines statistical and non-statistical data and allows one to answer questions, like “if we give a drug to a patient, i.e., intervene, will their chances of survival increase?” Statistical causality does not answer such questions, because it does not operate on the concept of intervention and only involves tools of data analysis. The causality in a statistical sense is a type of dependence, where we infer direction as a result of the knowledge of temporal structure and the notion that the cause has to precede the effect. We will focus here only on statistical causality measured by the information transfer approach.
